Taguchi’s DOE and artificial neural network analysis for the prediction of tribological performance of graphene nano-platelets filled glass fiber reinforced epoxy composites under the dry sliding condition

Author:

Sharma Nikhil,Kumar Santosh,Singh K.K.

Publisher

Elsevier BV

Subject

Surfaces, Coatings and Films,Surfaces and Interfaces,Mechanical Engineering,Mechanics of Materials

Reference56 articles.

1. Wear properties of carbon nanotubes filled epoxy polymers and woven glass fiber reinforced polymer composites;Talib;Pertanika J Sci Technol,2017

2. Analysis of symmetric and asymmetric glass fiber reinforced plastic laminates subjected to low-velocity impact;Singh;J Compos Mater,2015

3. Abrasive waterjet machining of fiber-reinforced composites: a review;Thakur;J Braz Soc Mech Sci Eng,2020

4. Polymer composites for tribological applications;Friedrich;Adv Ind Eng Polym Res,2018

5. Taguchi approach for characterization of three-body abrasive wear of carbon-epoxy composite with and without SiC filler;Subbaya;Compos Interfaces,2012

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